Portrait of Martin Vallières

Martin Vallières

Associate Academic Member
Associate Professor, McGill University, Department of Oncology
Research Topics
AI and Healthcare
Medical Machine Learning
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Biography

Martin Vallières is a researcher developing AI methods for Precision Medicine. His research expertise is at the intersection of AI and clinical sciences. His distinct research background contributed to the development of this “hybrid” type of expertise, one of key importance to accelerate the adoption of AI methods in the clinical environment.

Martin Vallières studied Physics Engineering at the Bachelor level. From 2010 to 2017, he then studied Medical Physics at the MSc and PhD levels and developed multiple predictive models for different cancer types. From 2017 to 2020, he pursued different postdoctoral internships in which he developed multimodal predictive models in oncology. On April 2020, he joined the Department of Computer Science at Université de Sherbrooke as an Assistant Professor and a Canada CIFAR AI Chair.

On August 2025, Martin Vallières changed affiliation and was appointed Associate Professor at the Medical Physics Unit of the Department of Oncology of McGill University. This new appointment will allow Martin Vallières to be in closer relation with clinical research teams and health domain end-users, a key point for the success of his research program.

Current Students

Hakima Laribi
PhD - Université de Sherbrooke
Principal supervisor :
Dan Poenaru
Website
Github
Olivier Lefebvre
PhD - Université de Sherbrooke
Principal supervisor :
Dan Poenaru
Github

Publications

Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1 Inhibition Immunotherapy for Glioblastoma
Elizabeth George
Elizabeth Flagg
Kuan-chun Chang
Hai-Yang Bai
H. Aerts
Martin Vallières
David A. Reardon
R.Y. Huang
BACKGROUND AND PURPOSE: Imaging assessment of an immunotherapy response in glioblastoma is challenging due to overlap in the appearance of t… (see more)reatment-related changes with tumor progression. Our purpose was to determine whether MR imaging radiomics-based machine learning can predict progression-free survival and overall survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy. MATERIALS AND METHODS: Post hoc analysis was performed of a multicenter trial on the efficacy of durvalumab in glioblastoma (n = 113). Radiomics tumor features on pretreatment and first on-treatment time point MR imaging were extracted. The random survival forest algorithm was applied to clinical and radiomics features from pretreatment and first on-treatment MR imaging from a subset of trial sites (n = 60–74) to train a model to predict long overall survival and progression-free survival and was tested externally on data from the remaining sites (n = 29–43). Model performance was assessed using the concordance index and dynamic area under the curve from different time points. RESULTS: The mean age was 55.2 (SD, 11.5) years, and 69% of patients were male. Pretreatment MR imaging features had a poor predictive value for overall survival and progression-free survival (concordance index  = 0.472–0.524). First on-treatment MR imaging features had high predictive value for overall survival (concordance index = 0.692–0.750) and progression-free survival (concordance index = 0.680–0.715). CONCLUSIONS: A radiomics-based machine learning model from first on-treatment MR imaging predicts survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy.
2022-04-27
American Journal of Neuroradiology (published)
doi.org